An air conditioner under test and its test method

Abstract

This invention relates to an air conditioner and its control method, belonging to the field of air conditioner technology. It includes an outdoor unit, an indoor unit, and a controller. The controller is configured to receive a test sequence generated based on test priority logic and control the air conditioner under test to sequentially execute multiple test items. The test priority logic includes acquiring test condition parameters, test energy consumption data, and risk correlation data; calculating the switching time when switching from the previous test item to the current test item to obtain an efficiency score; calculating the energy consumption score based on the test energy consumption data and the switching time; determining the risk score based on the risk correlation data; weighting the efficiency score, energy consumption score, and risk score, and combining them with a preset business value coefficient and a preset rectification correction coefficient to obtain a priority score; and sorting each test item according to the priority score to generate a test sequence. This achieves optimal sorting under multi-factor coupling, balancing failure cost and energy consumption cost.

Description

Technical Field

[0001] This invention relates to the field of air conditioner technology, and more specifically to an air conditioner under test and its testing method. Background Technology

[0002] In the development of air conditioners, testing is a crucial step in verifying product functionality, performance, and reliability. With increasing product complexity, the number of test items for a single unit has surged to hundreds, covering scenarios such as extreme high temperatures, torrential rain, and intelligent control. The order of testing directly impacts R&D efficiency and costs; improper sequencing can lead to delayed discovery of high-defect items, significantly increasing rectification costs. Furthermore, energy consumption due to changes in operating conditions fluctuates significantly depending on the order of testing.

[0003] Currently, test sequencing mainly relies on engineers' human experience, subjectively arranging tests based on factors such as risk level and energy consumption forecast in the test plan, or introducing static rules to simply rank tests according to risk level or temperature difference.

[0004] However, existing methods are difficult to balance multi-dimensional dynamic constraints such as risk, energy consumption, and business value manually, making it impossible to obtain a globally optimal solution. Furthermore, they rely on fixed parameters and empirical rules, and cannot iterate based on actual test data, resulting in a rigid and unadaptable sorting strategy. This leads to extended testing cycles and energy waste. Moreover, the failure to promptly interrupt defect propagation chains results in a sharp increase in subsequent repair costs. Summary of the Invention

[0005] This application provides an air conditioner under test and its control method to at least solve the problems in related technologies, such as low testing efficiency, high energy consumption, and increased post-repair costs due to the inability to coordinate multi-dimensional constraints and lack of self-learning ability.

[0006] In a first aspect, this application provides an air conditioner under test, comprising: The outdoor unit has an outdoor heat exchanger; Indoor unit, which has an indoor heat exchanger; A controller electrically connected to the outdoor unit and the indoor unit, the controller being configured to: receive a test sequence and control the air conditioner under test to sequentially execute multiple test items according to the test sequence; The test sequence is generated based on test priority logic, which includes: Based on the preset test requirements, obtain the test condition parameters, test energy consumption data and risk correlation data of each test item in the preset test environment; Based on the preset efficiency calculation logic and the test condition parameters, the switching time when switching from the previous test item to the current test item is calculated, and the efficiency score is obtained based on the switching time. Based on the preset energy consumption calculation logic, the energy consumption score is calculated according to the test energy consumption data and the switching time. Based on a preset risk calculation logic, a risk score is determined according to the risk-related data. After weighting the efficiency score, energy consumption score, and risk score, the priority score is obtained by combining the preset business value coefficient and the preset rectification correction coefficient. The test items are sorted according to their priority scores to generate the test sequence.

[0007] By constructing a test priority logic with dynamic priority score calculation as its core, the aim is to provide an automated sorting scheme under multi-dimensional constraints for air conditioner testing. By comprehensively considering multiple key factors such as efficiency, energy consumption, risk, business value, and emergency rectification, and by integrating these heterogeneous constraints into the same computational framework through weighted multiplication and coefficient multiplication, a globally optimized sequence that balances test time cost, energy cost, and quality risk cost can be systematically generated. This overcomes the fundamental defect that manual sorting cannot coordinate complex multi-objectives and realizes the optimal test decision from experience-driven to algorithm-driven.

[0008] In some embodiments, the test condition parameters include: The outdoor dry-bulb temperature and / or outdoor wet-bulb temperature are obtained by an outdoor temperature sensor located at the air inlet of the outdoor unit. The indoor dry-bulb temperature and / or indoor wet-bulb temperature are obtained by an indoor temperature sensor located at the return air vent of the indoor unit. The inlet and outlet water temperatures are obtained by a water temperature sensor installed on the water pipe connected to the outdoor heat exchanger.

[0009] By limiting the source and acquisition location of temperature parameters, accurate and targeted physical state inputs are provided for efficiency calculations; By obtaining the experimental condition parameters, the subsequent logic for calculating the switching time based on temperature difference is established on the basis of real physical measurements, thereby ensuring the accuracy of efficiency evaluation and the physical feasibility of test optimization strategies.

[0010] In some embodiments, the preset efficiency calculation logic is specifically as follows: Calculate the absolute value of the temperature difference between the current test item and the corresponding test condition parameters in the previous test item; The switching duration is obtained by multiplying the maximum absolute value of the temperature difference by a preset multiple. The switching time is standardized to obtain the efficiency score.

[0011] By using the logic of calculating the absolute value of the maximum temperature difference and combining it with a preset multiplier to determine the switching time, the complex multidimensional operating condition differences can be transformed into a unified and quantifiable time cost indicator.

[0012] By calculating the switching time, we can identify the dominant factors affecting the switching time, thus providing a common benchmark for efficiency comparisons between different test items. Furthermore, standardization eliminates the influence of dimensions, providing comparable efficiency scores for subsequent multi-factor weighted calculations.

[0013] In some embodiments, the preset efficiency calculation logic further includes: If the outdoor wet-bulb temperature or the indoor wet-bulb temperature is not present in the test condition parameters of the previous test item, but the outdoor wet-bulb temperature or the indoor wet-bulb temperature is present in the test condition parameters of the current test item, then a first additional duration is added to the switching duration. If the current test item is different from the preset target working condition of the previous test item, a second additional time is added to the switching time; The first additional duration and the second additional duration are independently added to the switching duration.

[0014] By introducing an additional time compensation mechanism for the transition from wet-bulb temperature to hot and cold operating conditions, the efficiency score calculation model can be made closer to the actual physical operation process of the test environment. It identifies and quantifies the significant additional time consumption caused by these special operations, and avoids the algorithm from underestimating the cost of such complex switching, thus improving the precision of the efficiency evaluation model and the rationality of the ranking results.

[0015] In some embodiments, the preset energy consumption calculation logic is specifically as follows: The total energy consumption and total runtime of the test environment within a preset statistical period are obtained, and the ratio of the total energy consumption to the total runtime is calculated to obtain the test energy consumption data. The energy consumption score is obtained by multiplying the test energy consumption data by the switching time.

[0016] By using the historical average energy consumption of the test environment as benchmark data and correlating it with the switching time to calculate the energy consumption score, the abstract energy consumption cost can be made concrete and quantifiable.

[0017] By calculating the energy consumption score, the algorithm can take energy consumption factors into account when sorting, providing key data support and decision-making basis for achieving the optimization goal of low energy consumption testing.

[0018] In some embodiments, the test priority logic further includes: Based on the preset test requirements, identify the key risk points, hardware change points, and software change points associated with the current test project; Identify the issues in other test items associated with the aforementioned key risk points, hardware change points, and software change points; Based on the aforementioned key risk points and each of the aforementioned problem points, confirm their corresponding key risk point level and problem point level; The risk-related data is obtained based on the key risk point level and the problem point level.

[0019] By acquiring key risk points and hardware / software change points, a comprehensive and structured information foundation can be provided for risk assessment. This ensures that the risk-related data not only includes the static risks of the project itself, but also the dynamic risks caused by design changes and historical issues. This lays a solid foundation for subsequently calculating a risk score that can comprehensively reflect the potential failure costs.

[0020] In some embodiments, the preset risk calculation logic specifically includes: Based on historical inspection records, confirm the number of rectifications required for each of the aforementioned issues; Based on a predefined rectification coefficient mapping table, obtain the rectification coefficient corresponding to the number of rectifications; Based on the predefined risk score mapping table, obtain the key risk score corresponding to the key risk point level, and the problem level score corresponding to each problem point level; Multiply each of the aforementioned problem level scores by its corresponding rectification coefficient to obtain the risk score for each problem; The risk score is obtained by selecting the maximum value among the key risk scores and the risk scores of each problem.

[0021] By multiplying the problem level score by the number of rectifications to amplify the risk contribution of stubborn problems, and taking the maximum value as the final risk score, the aim is to enable the risk assessment logic to dynamically reflect the severity and difficulty of solving the problem.

[0022] By calculating risk scores, the algorithm can intelligently identify recurring critical issues with high resolution costs and prioritize their related testing, thereby effectively controlling the high failure costs caused by defect propagation and rework.

[0023] In some embodiments, the test priority logic further includes: If the problem points associated with the test project are rectified, and the number of rectifications corresponding to the problem points is greater than or equal to 1, then the preset rectification correction coefficient is set as the first parameter. If the problem points associated with the test item are rectified, and the number of rectifications corresponding to the problem points is 0, then the preset rectification correction coefficient is set as the second parameter; If the test item has no associated problem points, then the preset rectification correction coefficient is set as the third parameter.

[0024] By directly linking the rectification correction coefficient to the rectification of the problem points and the number of rectifications in history, the algorithm can accurately identify and distinguish between newly emerging problems and stubborn old problems. By significantly increasing the priority of the latter, it ensures that test resources can be immediately directed to the most difficult and resource-intensive bottleneck links, thereby timely stopping the spread of the defect chain.

[0025] In some embodiments, the test priority logic further includes: The test order of the test sequence is adjusted based on a preset adjustment command to obtain the adjusted test sequence; Based on the adjusted test sequence, the priority score is calculated in reverse to obtain the correction values ​​for the number of rectifications, the level of key risk points, and the level of each problem point; The predefined rectification coefficient mapping table and the predefined risk score mapping table are updated based on the number of rectifications, the level of key risk points, and the correction values ​​of each problem point level.

[0026] By introducing a back-calculation and parameter update mechanism based on manual adjustment feedback, the core parameters of the algorithm can be freed from the limitations of static settings. This allows the scores of key risk point levels and problem point levels to be continuously self-calibrated and optimized as test data accumulates, thereby gradually improving the accuracy of priority ranking and the long-term adaptability of the algorithm, overcoming the shortcomings of rigid parameters in traditional methods.

[0027] Secondly, this application provides a control method for an air conditioner under test, including: Receive a test sequence; The air conditioner under test is controlled to perform multiple test items according to the test sequence; The test sequence is generated based on test priority logic, which includes: Based on the preset test requirements, obtain the test condition parameters, test energy consumption data and risk correlation data of each test item in the preset test environment; Based on the preset efficiency calculation logic and the test condition parameters, the switching time when switching from the previous test item to the current test item is calculated, and the efficiency score is obtained based on the switching time. Based on the preset energy consumption calculation logic, the energy consumption score is calculated according to the test energy consumption data and the switching time. Based on a preset risk calculation logic, a risk score is determined according to the risk-related data. After weighting the efficiency score, energy consumption score, and risk score, the priority score is obtained by combining the preset business value coefficient and the preset rectification correction coefficient. The test items are sorted according to their priority scores to generate the test sequence.

[0028] Compared to related technologies, the present invention provides a control method for an air conditioner under test. Through the test priority control method process of the air conditioner under test, it can take into account multiple factors such as efficiency, energy consumption, risk, business value and emergency rectification. Through quantitative calculation and weighted fusion, it generates a globally optimized test sequence, thereby systematically improving the scientific nature and execution efficiency of the test plan and reducing the dependence on external human experience.

[0029] Details of one or more embodiments of this application are set forth in the following drawings and description to make other features, objects and advantages of this application more readily apparent. Attached Figure Description

[0030] The accompanying drawings, which are included to provide an understanding of this application and form part of this application, illustrate exemplary embodiments of this application and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a schematic diagram of the structure of an air conditioner under test provided in an embodiment of this application; Figure 2 This is a schematic diagram of another air conditioner under test provided in an embodiment of this application; Figure 3 A flowchart illustrating the operation of a controller provided in an embodiment of this application; Figure 4 A schematic diagram illustrating the execution flow of test priority logic provided in an embodiment of this application; Figure 5 A schematic diagram illustrating the execution flow of another test priority logic provided in an embodiment of this application; Figure 6 A schematic diagram illustrating the execution flow of another test priority logic provided in an embodiment of this application; Figure 7 A schematic diagram illustrating the execution flow of another test priority logic provided in an embodiment of this application; Figure 8 A schematic diagram illustrating the execution flow of another test priority logic provided in an embodiment of this application; Figure 9 A schematic diagram illustrating the execution flow of another test priority logic provided in an embodiment of this application; Figure 10A flowchart illustrating a control method for an air conditioner under test, provided in an embodiment of this application.

[0031] In the picture: 1. Outdoor unit; 2. Indoor unit; 3. Controller. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of this application clearer, the application is described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application. Obviously, the accompanying drawings described below are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar scenarios based on these drawings without any inventive effort. Furthermore, it is understood that although the efforts made in this development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, any changes to design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as insufficient disclosure of the content of this application.

[0033] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0034] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “an,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or devices. The terms “connected,” “linked,” “coupled,” and similar words used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following objects are in an "or" relationship. The terms "first," "second," and "third" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects.

[0035] In the process of developing new air conditioners, the new product development and testing process includes several closely related and important steps, from compiling a test plan based on product functions, scenarios, software and hardware content and test standards, to prioritizing test items based on factors such as risk level and energy consumption, to rationally selecting laboratories to complete the planned production, and finally carrying out test execution.

[0036] Currently, with the rapid increase in the development and application of new technologies, functions, and scenarios for air conditioners, the complexity of product design has significantly increased.

[0037] To fully cover the testing needs derived from actual user scenarios, it is necessary to fully consider natural conditions such as extreme high and low temperatures, heavy rain, heavy snow, and typhoons, as well as functional interactions such as intelligence, sterilization, and rapid heating and cooling, and different application objects such as medical, residential, and data center equipment.

[0038] Based on the above testing requirements, the number of test items for a single model can reach hundreds. Therefore, it is particularly important to efficiently plan test priorities to achieve accurate testing in a shorter time and with lower energy consumption.

[0039] Test priority ranking is directly affected by two factors: failure cost and energy consumption cost.

[0040] The purpose of failure cost control is to block the defect propagation chain by sequential arrangement, so as to avoid the cost of problem localization soaring due to the cover-up of subsequent defects. Therefore, high-risk projects should be tested first to avoid rework. The purpose of energy cost control is to reduce the average temperature difference between test items by arranging them in sequence, thereby reducing equipment operating time and energy input. Therefore, the test sequence should be arranged according to the principle of small temperature difference.

[0041] However, in actual testing, the coupling relationship between the two types of factors is very complex, and test engineers cannot formulate an optimal ranking that takes both into account, which leads to energy waste.

[0042] In summary, in existing technologies, manual sorting is inefficient. When the number of test items for a single machine increases to hundreds, traditional methods cannot effectively balance failure costs and energy consumption costs. The high complexity of multi-factor coupling means that test priority must simultaneously consider dynamic factors such as risk level, energy consumption level, and business value, making it difficult to manually achieve the global optimal solution under multi-dimensional constraints. Static parameters lack adaptability. Traditional methods rely on fixed parameters and cannot be dynamically iterated and optimized based on actual test data, resulting in limited long-term applicability of the algorithm.

[0043] To address the aforementioned issues, this application proposes a test air conditioner and its control method. By comprehensively considering efficiency score, energy consumption score, risk score, business value coefficient, and rectification correction coefficient, priority scores are calculated to rank test items, thereby achieving optimal ranking under multi-factor coupling and balancing failure cost and energy consumption cost.

[0044] Figure 1 This is a schematic diagram of the structure of an air conditioner under test provided in an embodiment of this application. Figure 1 As shown, the air conditioner under test includes an outdoor unit 1. The outdoor unit 1 is used to compress the refrigerant and perform forced heat exchange with the outdoor air.

[0045] like Figure 2 As shown, an outdoor unit 1 is equipped with a compressor. The compressor drives the refrigerant to circulate in the air conditioning system and increases the refrigerant pressure and temperature through the compression process, providing power for the entire cooling or heating cycle.

[0046] Outdoor unit 1 is equipped with an outdoor heat exchanger. The outdoor heat exchanger is used as a condenser in cooling mode to release heat to the outdoor air and condense the high-temperature, high-pressure refrigerant, or as an evaporator in heating mode to absorb heat from the outdoor air and evaporate the low-temperature, low-pressure refrigerant.

[0047] Outdoor unit 1 is equipped with an outdoor fan. The outdoor fan is used to drive outdoor air to flow across the surface of the outdoor heat exchanger, enhance the heat exchange efficiency between the heat exchanger and the air, and promptly exhaust the airflow formed during the heat exchange process.

[0048] In some embodiments, the air conditioner under test further includes an indoor unit 2. The indoor unit 2 is used to provide a temperature-treated airflow into the room.

[0049] Indoor unit 2 is equipped with an indoor heat exchanger. The indoor heat exchanger is used as an evaporator in cooling mode to allow the low-temperature, low-pressure refrigerant to absorb heat from the indoor air, or as a condenser in heating mode to allow the high-temperature, high-pressure refrigerant to release heat to the indoor air.

[0050] Indoor unit 2 is equipped with an indoor fan. The indoor fan drives indoor air to circulate across the surface of the indoor heat exchanger, enabling efficient heat transfer between the air and the heat exchanger, and distributing the treated air into the indoor space at an appropriate speed.

[0051] In some embodiments, the air conditioner under test further includes a controller 3.

[0052] Controller 3 is electrically connected to outdoor unit 1 and indoor unit 2.

[0053] like Figure 3 As shown, controller 3 is configured to receive test sequences and control the air conditioner under test to execute multiple test items sequentially according to the test sequences; The test sequence is generated based on test priority logic; Furthermore, the test priority logic is a general optimization algorithm for product testing of the air conditioner under test. The test priority logic is deployed and runs in a central test management system that is independent of the air conditioner under test. Optionally, the central test management system can be the Dilab system; the Dilab system is an intelligent laboratory information management system used for the planning, scheduling and optimization of new product testing, which can shorten the testing cycle and reduce carbon emissions.

[0054] The central test management system calculates priority scores based on test priority logic, generates test sequences, and transmits the test sequences to the test workbench. The test workbench parses the test sequences and converts them into control commands. Control commands are sent to the laboratory's environmental equipment and the air conditioner under test, thereby controlling the air conditioner under test to perform the corresponding test items. During the testing process, testers can manually adjust the test order according to the actual situation. The central test management system can automatically record the reasons for the adjustment and iterate the test priority logic.

[0055] In some embodiments, such as Figure 4 As shown, based on the preset test requirements, the test condition parameters, test energy consumption data and risk correlation data of each test item in the preset test environment are obtained; Based on the preset efficiency calculation logic and test condition parameters, the switching time when switching from the previous test item to the current test item is calculated, and the efficiency score is obtained based on the switching time. Based on the preset energy consumption calculation logic, the energy consumption score is calculated according to the test energy consumption data and the switching time. Based on a pre-defined risk calculation logic, a risk score is determined according to risk-related data. After weighting the efficiency score, energy consumption score, and risk score, and combining them with the preset business value coefficient and preset rectification correction coefficient, the priority score is obtained. The test items are sorted according to their priority scores to generate a test sequence.

[0056] By constructing a test priority logic with dynamic priority score calculation as its core, the aim is to provide an automated sorting scheme under multi-dimensional constraints for air conditioner testing. By comprehensively considering multiple key factors such as efficiency, energy consumption, risk, business value, and emergency rectification, and by integrating these heterogeneous constraints into the same computational framework through weighted multiplication and coefficient multiplication, a globally optimized sequence that balances test time cost, energy cost, and quality risk cost can be systematically generated. This overcomes the fundamental defect that manual sorting cannot coordinate complex multi-objectives and realizes the optimal test decision from experience-driven to algorithm-driven.

[0057] In some embodiments, the outdoor dry-bulb temperature and / or outdoor wet-bulb temperature are obtained by an outdoor temperature sensor located at the air inlet of the outdoor unit 1. The indoor dry bulb temperature and / or indoor wet bulb temperature are obtained by an indoor temperature sensor installed at the return air vent of indoor unit 2. The inlet and outlet water temperatures are obtained by a water temperature sensor installed on the water pipe connected to the outdoor heat exchanger. The test condition parameters are obtained by combining the outdoor dry-bulb temperature and / or outdoor wet-bulb temperature, the indoor dry-bulb temperature and / or indoor wet-bulb temperature, the inlet water temperature, and the outlet water temperature.

[0058] By obtaining the experimental condition parameters, the subsequent logic for calculating the switching time based on temperature difference is established on the basis of real physical measurements, thereby ensuring the accuracy of efficiency evaluation and the physical feasibility of test optimization strategies.

[0059] In some embodiments, such as Figure 5 As shown, calculate the absolute value of the temperature difference between the current test item and the previous test item for each test condition parameter, select the maximum value of the absolute value of the temperature difference and multiply it by a preset multiple to obtain the switching time; The switching time is standardized to obtain an efficiency score.

[0060] By using the logic of calculating the absolute value of the maximum temperature difference and combining it with a preset multiplier to determine the switching time, the complex multidimensional operating condition differences can be transformed into a unified and quantifiable time cost indicator.

[0061] By calculating the switching time, we can identify the dominant factors affecting the switching time, thus providing a common benchmark for efficiency comparisons between different test items. Furthermore, standardization eliminates the influence of dimensions, providing comparable efficiency scores for subsequent multi-factor weighted calculations.

[0062] In some embodiments, if the test condition parameters in the previous test item do not include outdoor wet-bulb temperature or indoor wet-bulb temperature, and the test condition parameters in the current test item include outdoor wet-bulb temperature or indoor wet-bulb temperature, then a first additional duration is added to the switching duration. If the current test item is different from the preset target conditions of the previous test item, a second additional time will be added to the switching time. The first additional duration and the second additional duration are independently accumulated into the switching duration.

[0063] By introducing an additional time compensation mechanism for the transition from wet-bulb temperature to hot and cold operating conditions, the efficiency score calculation model can be made closer to the actual physical operation process of the test environment. It identifies and quantifies the significant additional time consumption caused by these special operations, and avoids the algorithm from underestimating the cost of such complex switching, thus improving the precision of the efficiency evaluation model and the rationality of the ranking results.

[0064] Furthermore, obtain the following six parameters for the previous test item and the current test item: outdoor dry-bulb temperature, outdoor wet-bulb temperature, indoor dry-bulb temperature, indoor wet-bulb temperature, inlet water temperature, and outlet water temperature. By calculating the difference between the above six parameters between two consecutive test items, six temperature difference values ​​are obtained, and the maximum absolute value is selected from the six temperature difference values. The switching duration is obtained by multiplying the maximum value of the selected absolute value with the preset value.

[0065] Optionally, the preset value can be set to 0.1, which means that it takes about 0.1 hours to adjust for every 1°C temperature difference; If the outdoor wet-bulb temperature or indoor wet-bulb temperature value of the previous test item is empty or 0, while the corresponding outdoor wet-bulb temperature or indoor wet-bulb temperature of the current test item has a valid value, then an additional 0.5 hours will be added to the switching time.

[0066] Adding a humidity control system requires preparation and stabilization, thus requiring additional time.

[0067] By comparing the preset target conditions of the previous test item with those of the current test item, it is determined whether to switch from cooling mode to heating mode or vice versa; if there is a switch, an additional 1.5 hours will be added to the switch time. Switching between cooling and heating modes involves significant actions of core components such as the compressor and system pressure balancing, thus taking the longest time.

[0068] Once a test project is completed in a real environment, the system records the actual adjustment time for switching from the previous test project to the current test project in the database. When it is necessary to calculate the efficiency score under the same switching conditions, the self-update logic is triggered; the same switching conditions include the same test items before and after the switch, the same laboratory, the same symbols, and other test conditions of the test items before and after the switch being completely consistent.

[0069] Retrieve all actual adjustment duration records that meet the switching conditions from the database, perform normal distribution analysis on the actual adjustment duration, and take the mean as the base value for a new and more accurate efficiency score. Use the base value of the efficiency score for the calculation of subsequent priority scores.

[0070] In some embodiments, such as Figures 6 to 7 As shown, the total energy consumption and total runtime of the preset test environment within the preset statistical period are obtained, and the ratio of total energy consumption to total runtime is calculated to obtain the test energy consumption data. The energy consumption score is obtained by multiplying the experimental energy consumption data by the switching time.

[0071] By using the historical average energy consumption of the test environment as benchmark data and correlating it with the switching time to calculate the energy consumption score, the abstract energy consumption cost can be made concrete and quantifiable.

[0072] By calculating the energy consumption score, the algorithm can take energy consumption factors into account when sorting, providing key data support and decision-making basis for achieving the optimization goal of low energy consumption testing.

[0073] Furthermore, the total energy consumption and total operating time of the laboratory under the preset test environment throughout the year are obtained. The average energy consumption per hour of the laboratory throughout the year is obtained by the ratio of total energy consumption to total operating time. The average energy consumption per hour of the laboratory throughout the year is used as the test energy consumption data. The initial value of the energy consumption score is obtained by multiplying the average hourly energy consumption of the laboratory throughout the year by the switching time.

[0074] During the test execution, once the environmental conditions switch from the previous target operating condition to the current target operating condition and stabilize, the total energy consumed during this specific switching process is actually measured and recorded in the database.

[0075] When it is necessary to calculate the energy consumption score under the same switching conditions, the self-update logic is triggered; the same switching conditions include the same test items before and after the switch, the same laboratory, the same symbols, and the other test conditions of the test items before and after the switch are completely the same. Retrieve all actual energy consumption records that meet the switching conditions from the database, calculate the arithmetic mean of the actual energy consumption, and obtain the typical energy consumption value for this specific switching. This typical energy consumption value will then be used as the energy consumption score for calculating the priority score.

[0076] In some embodiments, such as Figure 8 As shown, based on preset test requirements, key risk points, hardware change points, and software change points associated with the current test project are obtained; Identify the issues in other test items related to key risk points, hardware change points, and software change points; Based on the key risk points and each problem point, determine their corresponding key risk point level and problem point level; Risk correlation data is obtained based on the levels of key risk points and the levels of each problem point.

[0077] By acquiring key risk points and hardware / software change points, a comprehensive and structured information foundation can be provided for risk assessment. This ensures that the risk-related data not only includes the static risks of the project itself, but also the dynamic risks caused by design changes and historical issues. This lays a solid foundation for subsequently calculating a risk score that can comprehensively reflect the potential failure costs.

[0078] In some embodiments, such as Figure 9 As shown, based on historical inspection records, the number of rectifications required for each issue is confirmed. Based on a predefined rectification coefficient mapping table, obtain the rectification coefficient corresponding to the number of rectifications; Based on a predefined risk score mapping table, obtain the key risk score corresponding to the key risk point level, and the problem level score corresponding to each problem point level; Multiply the risk score of each problem by its corresponding rectification coefficient to obtain the risk score of each problem; The risk score is obtained by selecting the maximum value among the key risk scores and the risk scores of each issue.

[0079] By multiplying the problem level score by the number of rectifications to amplify the risk contribution of stubborn problems, and taking the maximum value as the final risk score, the aim is to enable the risk assessment logic to dynamically reflect the severity and difficulty of solving the problem.

[0080] By calculating risk scores, the algorithm can intelligently identify recurring critical issues with high resolution costs and prioritize their related testing, thereby effectively controlling the high failure costs caused by defect propagation and rework.

[0081] Furthermore, based on the preset testing requirements, identify the key risk points associated with the current testing project and confirm the key risk point level corresponding to each key risk point; Based on the predefined first risk score mapping table, the key risk score corresponding to the key risk point level is obtained; when there is no associated key risk point in the current test project, the key risk score is 0. Optionally, the first risk score mapping table includes the mapping relationship between the key risk point level and the key risk score; If the critical risk level is S, the corresponding critical risk score is 50. If the critical risk level is A, the corresponding critical risk score is 30. If the critical risk level is B, the corresponding critical risk score is 15. If the critical risk level is C, the corresponding critical risk score is 5. If the critical risk level is S, then the corresponding critical risk score is 0.

[0082] Based on the key risk points, identify the first problem points of other test items associated with the key risk points, and identify the first problem point level corresponding to the first problem point; Based on historical inspection records, confirm the first rectification count corresponding to the first problem point; Based on the predefined rectification coefficient mapping table, obtain the first rectification coefficient corresponding to the first rectification number; Based on the predefined second risk score mapping table, the first problem level score corresponding to the first problem point level is obtained; If the current test project has no associated critical risk points or other test projects corresponding to associated critical risk points have no problem points, then the first problem level score is 0; If other test projects have multiple problem points corresponding to the key risk points associated with the current test project, the weighted average of the scores of the multiple first problem level will be taken. Multiply the first problem level score by the first rectification coefficient to obtain the first problem risk score; Obtain the hardware change points associated with the current test project, identify the second problem points in other test projects associated with the hardware change points, and identify the second problem point level corresponding to the second problem point; Based on historical inspection records, confirm the second rectification count corresponding to the second problem point; According to the predefined rectification coefficient mapping table, obtain the second rectification coefficient corresponding to the second rectification number; Based on the predefined second risk score mapping table, the second problem level score corresponding to the second problem point level is obtained; If the current test item has no associated hardware change points or other test items corresponding to associated hardware change points have no problem points, then the second problem level score is 0; If there are multiple problem points in other test projects corresponding to the hardware change point associated with the current test project, the scores of the multiple second problem level are taken as a weighted average. Multiply the second problem level score by the second rectification coefficient to obtain the second problem risk score; Obtain the software change points associated with the current test project, identify the third issue points in other test projects associated with the software change points, and identify the third issue point level corresponding to the third issue point; Based on historical inspection records, confirm the third rectification order corresponding to the third problem point; According to the predefined rectification coefficient mapping table, obtain the third rectification coefficient corresponding to the third rectification number; Based on the predefined second risk score mapping table, the third problem level score corresponding to the third problem point level is obtained; If the current test item has no associated software change points or other test items corresponding to associated software change points have no problem points, then the third problem level score is 0; If other test projects have multiple issue points corresponding to the software change point associated with the current test project, the scores of the multiple third issue levels are taken as a weighted average. Multiply the third problem level score by the third rectification coefficient to obtain the third problem risk score; Optionally, the second risk score mapping table includes the mapping relationship between each problem point level and the problem level score; If each problem point is rated as S, then the corresponding problem rating score is 50. If each problem point is rated as A, then the corresponding problem rating score is 30. If each problem point is rated as B, then the corresponding problem rating score is 15. If each problem point is rated as level C, then the corresponding problem level score is 5. If each problem point is rated as S, then the corresponding problem rating score is 0.

[0083] Optionally, the rectification coefficient mapping table includes the mapping relationship between the number of rectifications and the rectification coefficient; If the number of rectifications is greater than or equal to 5, the corresponding rectification coefficient is 3. If the number of rectifications is greater than or equal to 3 and less than or equal to 4, the corresponding rectification coefficient is 2. If the number of rectifications is greater than or equal to 1 and less than or equal to 2, the corresponding rectification coefficient is 1.5. If the number of rectifications is 0, the corresponding rectification coefficient is 1.

[0084] Based on the calculated critical risk score, the first problem risk score, the second problem risk score, and the third problem risk score, select the maximum value among them to obtain the risk score; The risk score calculation model is as follows:

[0085] in, Risk score; Key risk score; The first question's grade score; The first rectification coefficient; The second question's grade score; This is the second rectification coefficient; The third question level score; This is the third rectification coefficient.

[0086] In some embodiments, the efficiency score, energy consumption score and risk score of all operating conditions are calculated and converted into standardized scores on a percentage scale, and then used to calculate the priority score. In some embodiments, weights are assigned to efficiency scores, energy consumption scores, and risk scores, with four assignment types set according to different priorities. When the allocation type is optimal energy consumption, the weight of the efficiency score is set to 0.2, the weight of the energy consumption score is set to 0.5, and the weight of the risk score is set to 0.3. By prioritizing the reduction of total test energy consumption, the decision weight of energy consumption score is significantly increased, guiding the algorithm to generate test sequences with the minimum temperature difference switching and the shortest equipment running time, thereby achieving energy saving and consumption reduction in the most direct way. When the allocation type is risk-optimal, the weight of the efficiency score is set to 0.2, the weight of the energy consumption score is set to 0.3, and the weight of the risk score is set to 0.5. By taking the control of product failure risk as the core objective and increasing the weight of risk scores, the algorithm is forced to prioritize high-risk test items, which can expose key defects as early as possible to block their propagation, thereby minimizing the cost of later rectification. When the allocation type is optimal efficiency, the weight of the efficiency score is set to 0.5, the weight of the energy consumption score is set to 0.3, and the weight of the risk score is set to 0.5. By prioritizing the reduction of overall testing time and amplifying the influence of efficiency scores, the algorithm prioritizes projects with similar operating conditions to reduce waiting time for environmental adjustments, thereby completing the testing process as quickly as possible. When the allocation type is comprehensive optimal, the weight of the efficiency score is set to 0.3, the weight of the energy consumption score is set to 0.35, and the weight of the risk score is set to 0.35. By balancing the three core costs of efficiency, energy consumption and risk, and assigning a slightly higher weight to energy consumption and risk while maintaining an equal weight to each other, the algorithm is guided to seek a globally optimal solution without severely sacrificing any single dimension. This approach is suitable for regular test scenarios without extreme emphasis. After weighting the efficiency score, energy consumption score, and risk score, and combining them with the preset business value coefficient and preset rectification correction coefficient, the priority score is obtained. The priority score calculation model is as follows:

[0087] in, Priority score; Efficiency score; The weighting for efficiency scores; Energy consumption score; The weighting of the energy consumption score; Weights for risk scores; Preset business value coefficient; Preset rectification and correction coefficients; In some embodiments, the preset business value coefficient is manually set according to predefined business rules, and the business value coefficient with the highest business value is defined as 1 as the upper limit of value.

[0088] Among them, the business rules stipulate that test projects with high business value have the characteristics of core performance verification, core parameter calibration, regulatory and security compliance, and core new function verification. Core performance verification includes energy efficiency tuning, such as determining APF and SEER values; Core parameter calibration includes items related to refrigerant charge accuracy, compressor high and low frequency operating range, and determination of key system pressure thresholds; Regulatory and safety compliance includes mandatory items such as electrical safety and noise standards; The verification of core new features includes the first application of intelligent algorithms and testing of new sterilization modules; According to the above rules, manually assign a business value coefficient between 0 and 1 to each test item, and keep two decimal places.

[0089] In some embodiments, if the problem points associated with the test project are rectified, and the number of rectifications corresponding to the problem points is greater than or equal to 1, then the preset rectification correction coefficient is set as the first parameter. If the issues associated with the test project are rectified, and the number of rectifications for the issues is 0, then the preset rectification correction coefficient is set as the second parameter. If the test item has no related issues, then set the preset rectification correction coefficient as the third parameter.

[0090] Optionally, the first parameter can be set to 100; When a new remediation effort is initiated for the same persistent issue, setting the first parameter instantly increases the priority score of all test items associated with that issue by two orders of magnitude. This forcibly interrupts the regular testing process, ensuring that R&D resources are immediately and absolutely prioritized for tackling the most challenging defects. This prevents the risk of soaring costs and project delays that could result from repeated failures. The second parameter can be set to 1.1; When the first rectification is initiated for a newly discovered problem, setting the second parameter can slightly increase the priority of the relevant test items. Without drastically disrupting the overall plan, the test sequence is guided to be moderately tilted toward the newly exposed risk points, so as to verify the remediation effect and assess its potential impact in a timely manner. The third parameter can be set to 1; When the test project is not associated with any ongoing rectification, the third parameter is set to maintain the original priority calculation, so that the ranking is determined entirely by conventional dimensions such as efficiency, energy consumption, risk and business value. This ensures that the test system can stably execute the predetermined multi-objective optimization strategy when there are no unexpected events.

[0091] By directly linking the rectification correction coefficient to the rectification behavior of creating new test projects and its historical number of rectifications, the algorithm can accurately identify and distinguish between newly emerging problems and stubborn old problems. By significantly increasing the priority of the latter, it ensures that test resources can be immediately directed to the most difficult and resource-intensive bottleneck links, thereby timely blocking the spread of defect chains.

[0092] In some embodiments, the test order of the test sequence is adjusted based on a preset adjustment instruction to obtain an adjusted test sequence; Based on the adjusted test sequence, the priority score is calculated in reverse to obtain the corrected values ​​for the number of rectifications, the level of key risk points, and the level of each problem point; The predefined rectification coefficient mapping table and the predefined risk score mapping table are updated based on the number of rectifications, the level of key risk points, and the correction values ​​of each problem point level.

[0093] Furthermore, when a test engineer manually adjusts the test order, an adjustment instruction is generated, the change type is recorded synchronously, and self-update logic is executed based on the change type. The test order of the test sequence is adjusted according to the adjustment instruction to obtain the adjusted test sequence; the new priority score is calculated based on the adjusted test sequence; the new priority score is the average of the priority score of the previous test item and the priority score of the next test item in the adjusted test sequence. Substitute the calculated new priority score into the priority score calculation model for inverse calculation; When the change type is an upgrade in the severity of a critical risk point, keep other parameters unchanged and calculate the critical risk score. When the change type is an upgrade in the severity of a problem, keep other parameters unchanged and calculate the score for each problem level. When the change type is "Severity of rectification number upgraded", keep other parameters unchanged and calculate the number of rectifications. When the change type is the calibration core parameter, other parameters are locked and the business value coefficient is calculated in reverse. The newly calculated value and the old value before adjustment are stored as a set of associated data in the corresponding database; The system is set to automatically start an iteration task every three months to continuously monitor the sample size in the database. When the number of valid samples of any type exceeds 10,000, the iteration will also be automatically triggered.

[0094] Extract all related data within a specified period from the database, calculate the change in each set of related data, and perform a normal distribution analysis with a confidence level of 95% on the dataset of all changes. This allows for the verification and updating of the corresponding risk score mapping table, rectification coefficient mapping table, or business value coefficient for each test item.

[0095] Secondly, such as Figure 10 As shown, this application provides a control method for an air conditioner under test, comprising: Receive a test sequence; The air conditioner under test is controlled to perform multiple test items according to the test sequence; The test sequence is generated based on test priority logic, which includes: Based on the preset test requirements, obtain the test condition parameters, test energy consumption data and risk correlation data of each test item under the preset test environment; Based on the preset efficiency calculation logic and test condition parameters, the switching time when switching from the previous test item to the current test item is calculated, and the efficiency score is obtained based on the switching time. Based on the preset energy consumption calculation logic, the energy consumption score is calculated according to the test energy consumption data and the switching time. Based on a pre-defined risk calculation logic, a risk score is determined according to risk-related data. After weighting the efficiency score, energy consumption score, and risk score, and combining them with the preset business value coefficient and preset rectification correction coefficient, the priority score is obtained. The test items are sorted according to their priority scores to generate a test sequence.

[0096] By using the test priority control method for the air conditioner under test, multiple factors such as efficiency, energy consumption, risk, business value, and emergency rectification can be considered. Through quantitative calculation and weighted fusion, a globally optimized test sequence is generated, thereby systematically improving the scientific nature and execution efficiency of the test plan and reducing the reliance on external human experience.

[0097] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0098] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. An air conditioner to be measured, characterized by, include: The outdoor unit has an outdoor heat exchanger; Indoor unit, which has an indoor heat exchanger; A controller electrically connected to the outdoor unit and the indoor unit, the controller being configured to: receive a test sequence and control the air conditioner under test to sequentially execute multiple test items according to the test sequence; The test sequence is generated based on test priority logic, which includes: Based on the preset test requirements, obtain the test condition parameters, test energy consumption data and risk correlation data of each test item in the preset test environment; Based on the preset efficiency calculation logic and the test condition parameters, the switching time when switching from the previous test item to the current test item is calculated, and the efficiency score is obtained based on the switching time. Based on the preset energy consumption calculation logic, the energy consumption score is calculated according to the test energy consumption data and the switching time. Based on a preset risk calculation logic, a risk score is determined according to the risk-related data. After weighting the efficiency score, energy consumption score, and risk score, the priority score is obtained by combining the preset business value coefficient and the preset rectification correction coefficient. The test items are sorted according to their priority scores to generate the test sequence.

2. The air conditioner to be measured according to claim 1, characterized in that, The test condition parameters include: The outdoor dry-bulb temperature and / or outdoor wet-bulb temperature are obtained by an outdoor temperature sensor located at the air inlet of the outdoor unit. The indoor dry-bulb temperature and / or indoor wet-bulb temperature are obtained by an indoor temperature sensor located at the return air vent of the indoor unit. The inlet and outlet water temperatures are obtained by a water temperature sensor installed on the water pipe connected to the outdoor heat exchanger.

3. The air conditioner under test according to claim 2, characterized in that, The preset efficiency calculation logic is as follows: Calculate the absolute value of the temperature difference between the current test item and the corresponding test condition parameters in the previous test item; The switching duration is obtained by multiplying the maximum absolute value of the temperature difference by a preset multiple. The switching time is standardized to obtain the efficiency score.

4. The air conditioner under test according to claim 3, characterized in that, The preset efficiency calculation logic also includes: If the outdoor wet-bulb temperature or the indoor wet-bulb temperature is not present in the test condition parameters of the previous test item, but the outdoor wet-bulb temperature or the indoor wet-bulb temperature is present in the test condition parameters of the current test item, then a first additional duration is added to the switching duration. If the current test item is different from the preset target working condition of the previous test item, a second additional time is added to the switching time; The first additional duration and the second additional duration are independently added to the switching duration.

5. The air conditioner under test according to claim 4, characterized in that, The preset energy consumption calculation logic is as follows: Obtain the total energy consumption and total runtime of the preset test environment within a preset statistical period, calculate the ratio of the total energy consumption to the total runtime, and obtain the test energy consumption data; The energy consumption score is obtained by multiplying the test energy consumption data by the switching time.

6. The air conditioner under test according to claim 1, characterized in that, The test priority logic also includes: Based on the preset test requirements, identify the key risk points, hardware change points, and software change points associated with the current test project; Identify the issues in other test items associated with the aforementioned key risk points, hardware change points, and software change points; Based on the aforementioned key risk points and each of the aforementioned problem points, confirm their corresponding key risk point level and problem point level; The risk-related data is obtained based on the key risk point level and the problem point level.

7. The air conditioner under test according to claim 6, characterized in that, The preset risk calculation logic is as follows: Based on historical inspection records, confirm the number of rectifications required for each of the aforementioned issues; Based on a predefined rectification coefficient mapping table, obtain the rectification coefficient corresponding to the number of rectifications; Based on the predefined risk score mapping table, obtain the key risk score corresponding to the key risk point level, and the problem level score corresponding to each problem point level; Multiply each of the aforementioned problem level scores by its corresponding rectification coefficient to obtain the risk score for each problem; The risk score is obtained by selecting the maximum value among the key risk scores and the risk scores of each problem.

8. The air conditioner under test according to claim 7, characterized in that, The test priority logic also includes: If the problem points associated with the test project are rectified, and the number of rectifications corresponding to the problem points is greater than or equal to 1, then the preset rectification correction coefficient is set as the first parameter. If the problem points associated with the test item are rectified, and the number of rectifications corresponding to the problem points is 0, then the preset rectification correction coefficient is set as the second parameter; If the test item has no associated problem points, then the preset rectification correction coefficient is set as the third parameter.

9. The air conditioner under test according to claim 7, characterized in that, The test priority logic also includes: The test order of the test sequence is adjusted based on a preset adjustment command to obtain the adjusted test sequence; Based on the adjusted test sequence, the priority score is calculated in reverse to obtain the correction values ​​for the number of rectifications, the level of key risk points, and the level of each problem point; The predefined rectification coefficient mapping table and the predefined risk score mapping table are updated based on the number of rectifications, the level of key risk points, and the correction values ​​of each problem point level.

10. A control method for an air conditioner under test, characterized in that, include: Receive a test sequence; The air conditioner under test is controlled to perform multiple test items according to the test sequence; The test sequence is generated based on the test priority logic defined in any one of claims 1 to 9.

Images